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Research Papers & Studies

Key research papers, studies, and reports referenced across Blueprint for an AI-First Company. Organized by topic area with source attribution where known.

Human-AI Collaboration

Studies on how humans and AI work together effectively -- and when collaboration fails. Central to Chapter 2 (The AI-First Mindset) and the Human-AI Collaboration framework.

Study Organization Key Finding Topic
Systematic review of human-AI teaming outcomes MIT Center for Collective Intelligence Reviewed 100+ studies and found that human-AI combinations don't, on average, outperform the best human-only or AI-only systems. Collaboration succeeds under three specific conditions. Human-AI collaboration effectiveness
Human-AI teaming study Nature Human Behaviour Identified conditions where human-AI teams succeed: tasks where humans outperform AI alone, content creation tasks, and creation tasks specifically involving generative AI. Conditions for effective collaboration
COHUMAIN framework Carnegie Mellon University Proposed viewing AI as a "partner under direction" rather than a teammate -- capable of strengthening capabilities but not replacing human judgment. AI collaboration design
AI coding productivity study METR (Model Evaluation and Threat Research) Randomized controlled trial found developers were 19% slower with AI assistance but predicted they'd be 24% faster -- a 43-point perception gap. Developer productivity measurement

AI Adoption & Business Impact

Large-scale surveys and analyses on AI adoption patterns across industries. Referenced throughout the book, particularly in chapters on strategy, operations, and teams.

Study Organization Key Finding Topic
Global Survey on AI McKinsey & Company 88% of companies fail with AI implementation. AI implementation failure rates
AI adoption studies MIT Sloan Identified the Buy, Boost, or Build framework for AI capability development. BCG deployed AI to consultants and reduced interview processing from two weeks to 2-3 days. AI adoption strategies
AI productivity research Accenture Trained all 700,000 employees in agentic AI. Marketing department saw 25% external brand value improvement and nearly one-third reduction in manual tasks. Enterprise AI training at scale
Enterprise AI spending survey Multiple sources (industry surveys) Average monthly AI spend jumped from $62,964 in 2024 to $85,521 in 2025. Companies spending over $100K monthly increased from 20% to 45%. AI cost trends
AI pilot failure analysis MIT (NANDA study) 95% of enterprise AI pilots fail to reach production with measurable value. The root cause is infrastructure and process failures, not model quality. Pilot-to-production gap
AI skills and productivity AWS AI skills boost productivity by at least 39% across organizations surveyed. Workforce productivity
Enterprise AI tool survey Zapier Only 35% of enterprise AI tools go through proper approval channels. 28% of enterprises use more than 10 different AI apps. 76% experienced negative outcomes from disconnected AI. AI tool sprawl

AI Risk & Safety

Research on AI risks, security vulnerabilities, and safety frameworks. Core to Chapter 11 (Ethics, Governance, and Risk) and the 7 AI Risks and Mitigations framework.

Study Organization Key Finding Topic
AI Risk Management Framework (AI RMF) NIST (National Institute of Standards and Technology) Four-function framework -- Govern, Map, Measure, Manage -- for identifying, assessing, and mitigating AI risks systematically. AI risk management
DeepSeek security evaluation Cisco Found a 100% attack success rate against DeepSeek-R1 -- the model failed to block any harmful prompts. Generates insecure code at four times the rate of competitors. AI model security
AI-generated code security Multiple research groups Only 55% of AI-generated code is secure. XSS vulnerabilities appear 86% of the time. SQL injection in 20% of cases. Code security
AI incident tracking Industry tracking AI incidents jumped 21% from 2024 to 2025 as companies expanded autonomy without expanding controls. AI incident trends
Model degradation study ML research 91% of ML models experience performance degradation without intervention due to data drift and concept drift. Model maintenance
AI hiring discrimination study Research (2024) AI screening favored white applicants over Black applicants with identical credentials 85% of the time. Algorithmic bias
Secrets leakage report Industry report (2024) 23.77 million secrets were leaked through AI systems -- a 25% increase from the prior year. Data security

Data Strategy & Competitive Advantage

Research on data flywheels, data moats, and AI economics. Referenced in Chapter 9 (Data Strategy) and the Data Flywheel and Data Moats frameworks.

Study Organization Key Finding Topic
Model collapse study Nature Models trained on AI-generated content exhibit "narrower range of output over time" -- each generation drifts further from reality. Synthetic data risks
AI startup failure analysis Industry research AI startups face a 92% failure rate within 18 months. 42% of companies abandoned most AI initiatives in 2025. AI startup survival
Data accuracy trends Industry survey Data accuracy in the U.S. declined from 63.5% in 2021 to just 26.6% in 2024. Data quality
AI wrapper economics Industry analysis Average AI wrapper operates at 25-60% gross margins versus 70-90% for traditional SaaS. 60-70% of AI wrappers generate zero revenue. AI business economics
AI customer service effectiveness Qualtrics (2025) AI-powered customer service fails at four times the rate of other AI tasks. Nearly one in five consumers see no benefits from AI customer service. Customer experience

AI Governance & Regulation

Regulatory frameworks and governance research. Referenced in Chapter 11 and the AI Governance Framework.

Framework / Study Organization Description Topic
EU AI Act European Union Tiered penalty regime: fines up to EUR 35M or 7% of global turnover for prohibited practices, 3% for high-risk violations. AI regulation
ISO 42001 ISO International standard for AI management systems, providing a framework for responsible AI development and deployment. AI standards
AI board oversight study Financial services industry 84% increase in board oversight disclosure around AI in 2024 across financial services. Corporate governance
AI Red Team taxonomy Microsoft Formalized taxonomy for AI agent failures published April 2025, aligning with the seven failure modes discussed in the book. AI safety testing
AI Fairness 360 IBM Open source toolkit for detecting and mitigating bias in AI models across the machine learning lifecycle. Bias detection
Fairlearn Microsoft Open source toolkit for assessing and improving fairness of AI systems. Fairness in AI